Deep Interactive Denoiser (DID) for X-Ray Computed Tomography

TL;DR

This paper introduces Deep Interactive Denoiser (DID), a lightweight testing-phase optimization method that generates multiple CT images with different noise-resolution tradeoffs, enhancing clinical flexibility and model generalizability.

Contribution

The paper proposes a novel testing-phase optimization for DL-based denoisers, enabling real-time generation of multiple image candidates for different clinical needs.

Findings

DID produces multiple image candidates with varied noise-resolution tradeoffs.

DID demonstrates strong generalizability across different network architectures and datasets.

Abstract

Low dose computed tomography (LDCT) is desirable for both diagnostic imaging and image guided interventions. Denoisers are openly used to improve the quality of LDCT. Deep learning (DL)-based denoisers have shown state-of-the-art performance and are becoming one of the mainstream methods. However, there exists two challenges regarding the DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs which sometimes are needed for different clinical tasks; 2) the model generalizability might be an issue when the noise level in the testing images is different from that in the training dataset. To address these two challenges, in this work, we introduce a lightweight optimization process at the testing phase on top of any existing DL-based denoisers to generate multiple image candidates with different noise-resolution tradeoffs suitable for different clinical tasks in real-time. Consequently, our method allows the users to interact with the denoiser to efficiently review various image candidates and quickly pick up the desired one, and thereby was termed as deep interactive denoiser (DID). Experimental results demonstrated that DID can deliver multiple image candidates with different noise-resolution tradeoffs, and shows great generalizability regarding various network architectures, as well as training and testing datasets with various noise levels.